title: Artificial Neural Networks as an alternative to traditional fall detection methods.
abstract: Falls are common events among older adults and may have serious consequences. Automatic fall detection systems are becoming a popular tool to rapidly detect such events, helping family or health personal to rapidly help the person that falls. This paper presents the results obtained in the process of testing a new fall detection method, based on Artificial Neural Networks (ANN). This method intends to improve fall detection accuracy, by avoiding the traditional threshold - based fall detection methods, and introducing ANN as a suitable option on this application.Also ANN have low computational cost, this characteristic makes them easy to implement on a portable device, comfortable to be wear by the patient.
authors.forename: Marcela
authors.initials: M
authors.lastname: Vallejo
authors.forename: Claudia V
authors.initials: CV
authors.lastname: Isaza
authors.forename: José D
authors.initials: JD
authors.lastname: López
mesh_terms: D061725:Accelerometry / Q000295:instrumentation / Q000379:methods*; D000058:Accidental Falls / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D001835:Body Weight; D001330:Electronic Data Processing; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D055815:Young Adult